Hud is a zero‑configuration runtime intelligence layer that runs alongside an entire codebase in production, continuously monitoring execution to automatically detect errors, performance regressions, CPU spikes, and new exception types. When an issue occurs, Hud captures full forensic context—including call stacks, parameters, and deployment metadata—and streams it to AI coding agents, which can generate precise, code‑level remediation suggestions or root‑cause reports. This eliminates the need for manual instrumentation, sampling, and human‑focused observability, enabling rapid, automated resolution of production problems.
Funding
Funding not disclosed
Founders
Product
Problem
Developers struggle to quickly identify the exact code paths and runtime conditions that cause errors, performance regressions, or CPU spikes in production because traditional observability tools provide sampled data, require manual instrumentation, and are designed for human analysis rather than automated remediation.
Solution
Hud provides a runtime intelligence layer that runs alongside the entire codebase in production without any configuration. It continuously monitors execution, automatically captures full forensic context—including call stacks, parameters, and deployment metadata—whenever an issue occurs. This rich context is streamed to AI coding agents, which can generate safe, code-level fixes or detailed root‑cause reports. By eliminating sampling and manual instrumentation, Hud delivers immediate, precise detection of performance regressions, error spikes, and new exception types, enabling both engineers and AI agents to resolve problems within minutes.
Target Audience
Primary customers are engineering teams and platform operators who need automated, production‑level root cause analysis, as well as AI‑driven development platforms that require accurate runtime data to generate code fixes.
Features
- Zero‑configuration deployment that instruments the full codebase in production within seconds
- Real‑time detection of performance regressions, error rate increases, CPU spikes, and new exception types
- Automatic collection of deep forensic context (exact call flow, parameters, deployment version) for each incident
- Integration with AI coding agents to produce actionable, code‑level remediation suggestions
- No sampling or manual thresholds; captures every relevant event with negligible overhead
- Supports large‑scale environments, running across millions of services simultaneously